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997 results for “awareness”
Dataset: iShares ESG Aware MSCI EM ETF (ESGE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Exploring Ethical Implications: Unraveling Factors Influencing Data Governance Awareness Behavior in Generative AI Chatbot
<p>Data set</p>
Dependency-aware code naturalness
<p>Docker image and data for the artifact of DAN (dependency-aware code naturalness)</p>
Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation
<p>Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation"</p> <p>This upload contains</p> <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> <p>used for the paper as pickle files and weights of trained models as zip files.</p> <p>Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training.</p> <p>Code for reading in the data sets, preprocessing and state estimation is available in the linked repository.</p> <p>To replicate the results of the paper follow these steps:</p> <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> <p><strong>Version 1.1:</strong></p> <p>Added data and model weights for the IEEE33 grid to improve comparability.</p> <p>For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states. </p> <p>The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder.</p> <p>Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000<em> </em>to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).</p>
Supplementary material 1 from: Lamarre GPA, Juin Y, Lapied E, Le Gall P, Nakamura A (2018) Using field-based entomological research to promote awareness about forest ecosystem conservation. Nature Conservation 29: 39-56. https://doi.org/10.3897/natureconservation.29.26876
Supplementary figures :
Multipath aware Scheduling for High Reliability and Fault Tolerance in Low Power Industrial Networks
<p>The Industrial Internet of Things is expected to enable the Industry 4.0 through the large deployment of low-<br> power devices. However, industrial applications require most of the time high reliability close to 100%, and<br> low end-to-end delays. Thus, most industrial wireless networks rely on a strict schedule of the transmissions<br> to avoid collisions, and implement frequency hopping to combat external interference. In multihop topologies,<br> the network has to decide both when the transmissions have to be scheduled, and which router can forward<br> the packets. To be fault-tolerant, multipath routing consists in exploiting several paths in parallel. We<br> exploit here a braided path routing structure, where each router has several possible next hops. Thus, we<br> can cope with any fault along the path, while still providing a remaining operational path. We propose<br> also a scheduling algorithm, where multiple transmitters are attached to a single cell, to the same receiver.<br> The schedule is constructed such that only one transmitter is active at a time, and is consequently collision-<br> free. Mutualizing the same cell for several transmitters reduces the energy consumption and increases the<br> network capacity.</p>
IMPORTANCE OF RASING STUDENTS' AWARENESS ABOUT CLIMATE JUSTICE IN LANGUAGE CLASSES
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Poster_The_Impact-Aware_Robotics_Database
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Time Aware Simulated Annealing Experimental Results
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Leveraging DAGs to improve context-sensitive and abundance-aware tree estimation
<p>Simulation, inference results, plots, and instructions for running code to reproduce results.</p>
Enabling Design of Secure IoT Systems with Trade-Off-Aware Architectural Tactics
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Exploring the Key Success Factors of Cyber Security Awareness in Distributed Teams
<p>This is the data set for the shared paper with the journal</p>
DATA for Evaluating Scale-Aware Boundary Layer Similarity Functions and Their Mechanisms in Tropical Cyclone Modeling Using Idealized Large-Eddy Simulations
<p>data.xlsx has the data to make Figs.1-4</p> <p> </p>
awareness about contraception
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CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies
<p><strong>Abstract:</strong></p> <p>We introduce CALIPERS, a Fluorescence Ubiquitin Cell Cycle Indicator enabling cell cycle-aware phenotyping based on multiplexed live-cell imaging. We validate CALIPERS in three- and four-color reporter lines (HaCaT) and human induced pluripotent stem cells (WTC-11), co-expressing genetically encoded structural and functional fluorescent sensors. To exemplify the broad applications of CALIPERS, we show cell cycle-aware phenotyping in live-cell imaging applications ranging from proliferation and migration to cardiac-specific maturation and drug testing.</p> <p>Code submission: <a href="https://github.com/alessandro-bertero/FUCCIplex">https://github.com/alessandro-bertero/FUCCIplex.git</a></p> <p><strong>Dataset description:</strong></p> <ol> <li>This dataset includes all data and the additional files to recreate the sc-RNAseq analysis in our manuscript. The Rmd files include the Seurat QC pipeline to preprocess the counts data from cellranger (<strong>QC_Seurat_gitgub_polished.RmD</strong>) and the monocle pipeline to re-create the clusters and pseudotime analysis (<strong>monocle_github_polished.Rmd</strong>).</li> <li><strong>Data obtained from Cellranger 7.2 pipeline</strong> are also available here including all the 3 files, usually placed in the path "outs/count/filtered_feature_bc_matrix" by cellrancer. This includes: <strong>barcodes.tsv.gz, features.tsv.gz and matrix.mtx.gz</strong> (alternatively the h5 file filtered_feature_bc_matrix.h5 can be loaded directly in Seurat v5).</li> <li>We also added few RData files to recreate our graphs and follow our pipeline smoothly. <strong>240111_seurat_before_clean.RData, 240111_seurat_clean.RData</strong> and <strong>240111_seurat_processed.RData</strong> are the files created by the codes in QC_Seurat_gitgub_polished.RmD pipeline. Then, <strong>240118_monocle_cds_clustered_proteincoding_PSEUDOTIME_clean_gene_info.RData</strong> is the file set created by the<strong> </strong>monocle_github_polished.Rmd pipeline.</li> </ol> <p>We also suggest to use this pipeline within a docker container by pulling our docker image with R studio:</p> <p><strong>docker pull hedgelab/rstudio-hedgelab:latest</strong></p>
Knowledge and awareness of Covid-19 in eye care
<p><span><b>Background:</b> Nepal is currently under a lockdown due to the COVID-19 pandemic. There are concerns regarding misinformation circulating on social media. This study aimed to analyse the knowledge and awareness of COVID-19 amongst eye care professionals in Nepal. </span></p> <p><span><b>Methodology</b>: We invited 600 participants from 12 ophthalmic centres across Nepal to complete a qualitative, anonymous online survey. Altogether, 25 questions (both open and closed-ended) were used. An overall performance score was calculated from the average of the 12 Knowledge questions for all the participants.</span></p> <p><span><b>Results: </b>Of the 600 eye care professionals invited, 310 (51%) participated in the survey. The symptoms of COVID-19 were known to 94%, whilst only 49% of the participants were aware how the disease was transmitted, with 54% aware that anyone can be infected with <span>SARS-CoV-2</span>. Almost 98% of participants recognized the World Health Organization's (WHO) awareness message, but surprisingly, 41% of participants felt that consumption of hot drinks helps to destroy the virus, in contradiction to WHO information. Importantly, 95% of the participants were aware of personal protective equipment (PPE) and what the acronym stands for. Social distancing was felt to be key to limiting the disease spread; whilst 41% disagreed that PPE should be mandatory for eye care practitioners.</span></p> <p><span><b>Conclusion: </b>There is still considerable scope to improve the knowledge of COVID-19 amongst ophthalmic professionals in Nepal. Opinion is also split on measures to prevent transmission, with misinformation potentially fuelling confusion. It is recommended to follow WHO and national guidelines, whilst seeking published scientific evidence behind any unofficial statements, to accurately inform one's clinical practice.</span></p>
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</p>
Assemblies generated in the paper "phasebook: haplotype-aware de novo assembly of diploid genomes from long reads"
<p>Assemblies generated in the paper "phasebook: haplotype-aware de novo assembly of diploid genomes from long reads"</p>
Context-Aware Retrieval-based Deep Commit Message Generation Dataset
<p>dataset</p>
Supplementary material 2 from: Green S, Dehnen-Schmutz K, Drakulic J, Eschen R, Orazio C, Douma JC, Lundén K, Colombari F, Jactel H (2023) Awareness, detection and management of new and emerging tree pests and pathogens in Europe: stakeholders' perspectives. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 9-40. https://doi.org/10.3897/neobiota.84.95761
Supplementary tables and figures
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.